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Computer Vision – ECCV 2016

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Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Reflection Symmetry Detection via Appearance of Structure Descriptor
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    Chapter 2 Faceless Person Recognition: Privacy Implications in Social Media
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    Chapter 3 Segmental Spatiotemporal CNNs for Fine-Grained Action Segmentation
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    Chapter 4 Structure from Motion on a Sphere
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    Chapter 5 Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark
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    Chapter 6 MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
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    Chapter 7 Hierarchical Beta Process with Gaussian Process Prior for Hyperspectral Image Super Resolution
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    Chapter 8 A 4D Light-Field Dataset and CNN Architectures for Material Recognition
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    Chapter 9 Graph-Based Consistent Matching for Structure-from-Motion
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    Chapter 10 All-Around Depth from Small Motion with a Spherical Panoramic Camera
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    Chapter 11 On Volumetric Shape Reconstruction from Implicit Forms
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    Chapter 12 Multi-attributed Graph Matching with Multi-layer Random Walks
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    Chapter 13 Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
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    Chapter 14 A Neural Approach to Blind Motion Deblurring
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    Chapter 15 Joint Face Representation Adaptation and Clustering in Videos
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    Chapter 16 Uncovering Symmetries in Polynomial Systems
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    Chapter 17 ATGV-Net: Accurate Depth Super-Resolution
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    Chapter 18 Indoor-Outdoor 3D Reconstruction Alignment
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    Chapter 19 The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
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    Chapter 20 A Simple Hierarchical Pooling Data Structure for Loop Closure
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    Chapter 21 A Versatile Approach for Solving PnP, PnPf, and PnPfr Problems
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    Chapter 22 Depth Map Super-Resolution by Deep Multi-Scale Guidance
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    Chapter 23 SEAGULL: Seam-Guided Local Alignment for Parallax-Tolerant Image Stitching
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    Chapter 24 Grid Loss: Detecting Occluded Faces
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    Chapter 25 Large-Scale R-CNN with Classifier Adaptive Quantization
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    Chapter 26 Face Detection with End-to-End Integration of a ConvNet and a 3D Model
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    Chapter 27 Large Scale Asset Extraction for Urban Images
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    Chapter 28 Multi-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling
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    Chapter 29 Shading-Aware Multi-view Stereo
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    Chapter 30 Fine-Scale Surface Normal Estimation Using a Single NIR Image
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    Chapter 31 Pixelwise View Selection for Unstructured Multi-View Stereo
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    Chapter 32 Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
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    Chapter 33 Generic 3D Representation via Pose Estimation and Matching
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    Chapter 34 Hand Pose Estimation from Local Surface Normals
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    Chapter 35 Abundant Inverse Regression Using Sufficient Reduction and Its Applications
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    Chapter 36 Learning Diverse Models: The Coulomb Structured Support Vector Machine
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    Chapter 37 Pose Hashing with Microlens Arrays
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    Chapter 38 The Fast Bilateral Solver
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    Chapter 39 Phase-Based Modification Transfer for Video
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    Chapter 40 Colorful Image Colorization
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    Chapter 41 Focal Flow: Measuring Distance and Velocity with Defocus and Differential Motion
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    Chapter 42 An Evaluation of Computational Imaging Techniques for Heterogeneous Inverse Scattering
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    Chapter 43 Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
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    Chapter 44 Fast Guided Global Interpolation for Depth and Motion
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    Chapter 45 Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs
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    Chapter 46 Multi-view Inverse Rendering Under Arbitrary Illumination and Albedo
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    Chapter 47 DAPs: Deep Action Proposals for Action Understanding
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    Chapter 48 A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
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    Chapter 49 Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers
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    Chapter 50 Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
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    Chapter 51 Going Further with Point Pair Features
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    Chapter 52 Webly-Supervised Video Recognition by Mutually Voting for Relevant Web Images and Web Video Frames
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    Chapter 53 HFS: Hierarchical Feature Selection for Efficient Image Segmentation
Attention for Chapter 35: Abundant Inverse Regression Using Sufficient Reduction and Its Applications
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Chapter title
Abundant Inverse Regression Using Sufficient Reduction and Its Applications
Chapter number 35
Book title
Computer Vision – ECCV 2016
Published in
Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision, October 2016
DOI 10.1007/978-3-319-46487-9_35
Pubmed ID
Book ISBNs
978-3-31-946486-2, 978-3-31-946487-9

Hyunwoo J. Kim, Brandon M. Smith, Nagesh Adluru, Charles R. Dyer, Sterling C. Johnson, Vikas Singh


Statistical models such as linear regression drive numerous applications in computer vision and machine learning. The landscape of practical deployments of these formulations is dominated by forward regression models that estimate the parameters of a function mapping a set of p covariates, x , to a response variable, y. The less known alternative, Inverse Regression, offers various benefits that are much less explored in vision problems. The goal of this paper is to show how Inverse Regression in the "abundant" feature setting (i.e., many subsets of features are associated with the target label or response, as is the case for images), together with a statistical construction called Sufficient Reduction, yields highly flexible models that are a natural fit for model estimation tasks in vision. Specifically, we obtain formulations that provide relevance of individual covariates used in prediction, at the level of specific examples/samples - in a sense, explaining why a particular prediction was made. With no compromise in performance relative to other methods, an ability to interpret why a learning algorithm is behaving in a specific way for each prediction, adds significant value in numerous applications. We illustrate these properties and the benefits of Abundant Inverse Regression (AIR) on three distinct applications.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 27%
Student > Master 2 18%
Student > Bachelor 1 9%
Lecturer 1 9%
Other 1 9%
Other 0 0%
Unknown 3 27%
Readers by discipline Count As %
Computer Science 6 55%
Mathematics 1 9%
Psychology 1 9%
Unknown 3 27%